Background: The goal of this proposal is to improve the performance of the Gail breast cancer risk prediction model 2 for women 35-49 years of age, by the addition of biomarkers (testosterone or free testosterone, and/or Mullerian Inhibiting Substance (MIS)). MIS, which is detectable only in premenopausal women, was associated with a large increase in risk of both pre- and post-menopausal breast cancer in the only prospective study to date (OR = 9.8, 95% CI = 3.3 to 28.9 for the highest vs. lowest quartile). Premenopausal levels of testosterone and free testosterone have also been consistently shown to be positively associated with increased risk of both pre- and post-menopausal breast cancer. All three biomarkers vary little during the menstrual cycle, can be measured relatively inexpensively, and have good temporal reliability, i.e. a single measurement is reasonably representative of a woman's long-term average level, making them good candidates for inclusion in a risk prediction model. Aims: 1) To evaluate the association of premenopausal levels of MIS with breast cancer risk; 2) To assess whether adding biomarkers (testosterone or free testosterone, and/or MIS) to the factors included in the Gail model 2 improves the prediction performance of the model for women 35-49 years of age. Methods: The study will use the resources of eight prospective cohorts which collected serum or plasma from healthy young women and followed them up for incidence of breast cancer (Breakthrough Generations Study; CLUE II; Columbia, MO Serum Bank; Guernsey Cohort; Nurses' Health Study II; New York University Women's Health Study; Northern Sweden Mammary Screening Study; ORDET). For aim 1, a 1:2 nested case:control study (2500 cases) will be conducted. For aim 2, the performance of risk prediction models including one or two biomarkers, in addition to factors included in the Gail model 2 (age, age at menarche, age at first live birth, number of previous breast biopsies and number of first degree relatives with a history of breast cancer), will be compared to the Gail model 2 with respect to calibration and discriminatory accuracy. Impact: An improved risk prediction model will help women make more informed decisions regarding breast cancer screening and chemoprevention. This is particularly relevant to younger women for whom guidelines on screening are inconsistent. Further, tamoxifen, which is approved in the US for prevention of breast cancer in women age 35 and older who are at increased risk of breast cancer, is most likely to benefit younger women because they are at lower risk than older women of the adverse effects of tamoxifen.